System and method for capturing interaction data relating to a host application

ABSTRACT

Systems and methods for capturing interaction data relating to a host application (app) implemented on a mobile device are disclosed. A tracking module is embedded in the host application. Interaction data relating to the host application is captured on the mobile device with the tracking module. The captured interaction data is transmitted from the mobile device.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation application of U.S. application Ser.No. 14/216,889 filed Mar. 17, 2014, which claims the benefit of U.S.Provisional Patent Application Ser. No. 61/793,943 filed Mar. 15, 2013.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present application relates generally to systems and methods forcapturing interaction data on a mobile device. More specifically, thepresent application relates to systems and methods for capturing ofinteraction data relating to a host application implemented on themobile device.

2. Description of Related Art

It is desirable for an entity to be able to observe and/or collect andanalyze interaction data relating to the webpages of the entity.Commercial software for collecting and analyzing interaction datarelating to webpages is known. One such example is disclosed in U.S.patent application Ser. No. 13/746,231, which was filed on Jan. 21,2013, by the Applicants of the subject patent application, and which isexpressly incorporated herein by reference in its entirety.

However, such known tools do not permit the collection or analysis ofdetails of interaction data relating to a host application, known morecommonly as an “app.” Apps are software applications that are designedspecifically for mobile devices, such as, but not limited to, smartphones and tablets. Both apps and websites are accessible on the mobiledevices. However, unlike a webpage, an app operates in a completely freeenvironment once embedded in the mobile device. As such, the app hasaccess to nearly everything supported by the mobile device. Furthermore,unlike a webpage that must be rendered within a browser, an app must bedownloaded and installed on the mobile device.

Known tools and methods for capturing interaction data relating towebpages cannot be utilized to capture interaction data relating to appsembedded on mobile devices. The reason is predominately a result of thefundamental differences between a webpage and an app.

Because of such limitations, it is desirable to provide systems andmethods that provide capturing of interaction data relating to a hostapplication (app) on a mobile device. It is also desirable to providesystems and methods that provide such capturing of interaction datawithout significant performance impact on the mobile device.

BRIEF DESCRIPTION OF THE DRAWINGS

Advantages of the present invention will be readily appreciated, as thesame becomes better understood by reference to the following detaileddescription, when considered in connection with the accompanyingdrawings.

FIG. 1 is an architectural view of a system for capturing and replayinginteraction data relating to a host application implemented on a mobiledevice, according to one embodiment of the present invention;

FIG. 2 is a computer architectural view of the mobile device of thesystem, according to one embodiment of the present invention;

FIG. 3 is a flow chart illustrating a method for capturing and replayinginteraction data relating to the host application implemented on themobile device, according to one embodiment of the present invention;

FIG. 4 is a computer process view of a method of capturing visualinteraction data, according to one embodiment of the present invention;

FIG. 5 is a computer process view of a method of capturing visualinteraction data, according to another embodiment of the presentinvention; and

FIGS. 6-8 are computer process views of a method of encoding visualinteraction data, according to one embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Referring to the Figures, wherein like numerals indicate like orcorresponding parts throughout the several views, a system for capturingand replaying interaction data relating to a host applicationimplemented on a mobile device is generally shown at 10.

As illustrated in FIG. 1, the system 10 includes a mobile device 12. Themobile device 12 may include any suitable mobile device, such as, butnot limited to a smart phone or a tablet. Examples of the mobile device12 non-exclusively include the iPad® or iPhone® by Apple®, Inc.

The mobile device 12 is connected to a network 14. The network 14 mayinclude, for example, a local area network (LAN), wide area network(WAN), world wide web (WWW), or the Internet. As shown in FIG. 2, themobile device 12 includes an operating system 34, such as, but notlimited to, iOS® by Apple® or Android® by Google®.

The system 10 includes a tracking server 16 that is connected to thenetwork 14. The tracking server 16 is in communication with the mobiledevice 12 through the network 14. The system 10 further includes ananalysis computer 18 that is connected to the network 14. The analysiscomputer 18 is in communication with the tracking server 16 through thenetwork 14.

As illustrated in FIG. 2, a host application 20 is implemented on themobile device 12. The host application 20 is commonly known in the artas an “app.” The host application 20 is generally a self-containedprogram designed to fulfill a particular purpose. The host application20 is distinguished from a mobile webpage or a browser-based extension.Unlike a mobile webpage or a browser-based extension, the hostapplication 20 operates independently from a web browser and includes aseparate and dedicated user interface (UI). The host application 20 isconfigured to operate in a completely free environment once embedded inthe mobile device 12. As such, the host application 20 has full accessto whatever the mobile device 12 supports. Also, unlike a web page, thehost application 20 is downloaded and installed on the mobile device 12.

A user 22 generally operates and interacts with the mobile device 12,and thus by extension, has access to and interacts with the hostapplication 20. The host application 20 may fulfill any suitableparticular purpose. For example, the host application 20 may provide agame, a social networking medium, an interface to news or weather, andthe like.

A method for capturing and replaying interaction data relating to thehost application 20 implemented on the mobile device 12 is generallyshown at 70 in FIG. 3. At step 72, interaction data relating to the hostapplication 20 is captured on the mobile device 12. The interaction datamay be subsequently replayed on the analysis computer 18, as will bedescribed below.

Interaction data may be initiated by the user 22. For example, the user22 may interact with the host application 20 by actively interfacingwith, e.g., pressing, a display 28 on the mobile device 12.Alternatively, interaction data may be initiated by the host application20. For example, the host application 20 may refresh images on thedisplay 28. This may be done so independent of any interaction from theuser 22. As such, the term “interaction data” is not limited to datathat is initiated by the user 22.

Interaction data may include visual interaction data, which is likewisecaptured on the mobile device 12. In one embodiment, screen displayimages of the host application 20 are provided on the display 28 on themobile device 12. The visual interaction data may include “screen-shots”or “screen-captures” of such screen display images of the hostapplication 20 at any given time. Screen display images may be capturedin any suitable format, such as a Bitmap (BMP) format, or the like. Aswill be described in detail below, such screen display images may beencoded into a movie format, e.g., MPEG, AVI, WMV, MP4, or thedifferences from one screen display image to the next may be stored offin raw form for transmission later.

In one embodiment, capturing visual interaction data includes capturinga change to the image. In one instance, the change to the image may beinitiated by the user 22. Alternatively, the change to the image may beinitiated by the host application 20 such that the change is initiatedindependent of the user 22. In one embodiment, capturing the change tothe image includes capturing a visual change to the image.Alternatively, capturing the change to the image includes capturing anon-visual change to the image.

In another embodiment, visual interaction data may include camera data.Camera data may include images or video captured by a camera on themobile device 12. In one instance, such camera data may be utilized tocapture facial expressions of the user during interaction with the hostapplication 20.

Interaction data may also include non-visual interaction data. In suchsituations, non-visual interaction data is captured on the mobile device12. In one embodiment, non-visual interaction data may includemeta-data, which includes one, or a combination of, a touch-event,gesture data, GPS coordinate data, audio data, accelerometer data, andorientation data. A touch-event generally includes the user's 22movements and selections, such as pointing-clicks and scrolling. Gesturedata generally includes the user's 22 flicking, panning, minimizing, orstretching the images on the display 28. GPS coordinate data includespositional information corresponding to where the mobile device 12 islocated at any give time. Audio data may include audio that was playedto the user 22 during interactions with the mobile device 12.Orientation and/or accelerometer data generally include informationrepresenting how the mobile device 12 is being held. For example, suchdata may represent whether the mobile device 12 is held in a portrait orlandscape format and/or at a pose with respect to a vertical and/orhorizontal axis. In another embodiment, non-visual interaction dataincludes barometer data, which may be data relating to augmentation ofGPS on the mobile device 12. Additionally, non-visual interaction datamay include compass data, including navigational data for supplementingGPS on the mobile device 12. Furthermore, non-visual interaction datamay include camera data, such as data relating to when the camera isforward-facing.

The non-visual interaction data of the user session may be captured as asequence of events that are recorded into a buffer along with their timeindexes. Each event contains information pertaining to when the eventoccurred (a timestamp) and what event occurred (an event type). Eventscan also contain an arbitrary payload of data. In one example, theformat of the event, represented in JSON format, would be as follows:

  {  ″timestamp″: 1343936783,  ″type″: ″gesture type″,  ″data″: {   ...//event-type-specific structure  } }

In one example, the “timestamp” property of the non-visual interactiondata could be Unix time. The “type” and “data” properties of thenon-visual interaction data may be used in processing at the trackingserver 16. The “data” property of the non-visual interaction data may bea JSON data structure. Alternate formats of the “data” property includeXML or serialized object model.

On the Android platform, for example, touch events can be captured byregistering as a touch event listener on a root view, such as a top viewin a view hierarchy. Some types of subviews, such as text boxes and listviews, may not propagate touch events up the view hierarchy by default.In such situations, touch events can be captured by registering as atouch event listener directly on the view in question. According to oneway of achieving this, the view hierarchy is “walked” from the root viewdown to search for views that require this approach.

Touches may be recorded in groups as part of a larger gesture event.Each touch in a gesture may be uniquely identified to associate a giventouch at some time with the finger it belongs to as part of thatgesture. Device orientation and layout (interface) orientation can alsobe recorded in the event stream.

In one embodiment, capturing interaction data includes capturing onlyvisual interaction data. In another embodiment, capturing interactiondata includes capturing only non-visual interaction data. In yet anotherembodiment, capturing interaction data includes capturing both visualinteraction data and non-visual interaction data.

Visual interaction data may be captured at a different time thannon-visual interaction data. Alternatively, visual interaction data andnon-visual interaction data may be captured simultaneously. In oneexample, an image may be captured and simultaneously encoded with atime-stamp including a sequence of characters or encoded informationidentifying when the image is captured.

As shown in FIG. 2, a tracking module 30 is provided. In one embodiment,the tracking module 30 is embedded in the host application 20. When thetracking module 30 is embedded in the host application 20, it is not astand-alone software application by itself.

The tracking module 30 is configured to capture interaction data. Inother words, the tracking module 30 is configured to capture visualinteraction data and/or non-visual interaction data. The tracking module30 is also configured to capture times associated with the visualinteraction data and/or non-visual interaction data.

In one embodiment, the tracking module 30 is further defined as anapplication programming interface (API). The API is configured to accessdatabases and hardware of the mobile device 12. The API is source-codebased and specifies a set of functions or routines that interact withthe host application 20 for capturing of interaction data. The API mayinclude a library that includes specifications for suitable routines,data structures, object classes, and variables. The tracking module 30may have other configurations without departing from the scope of theinvention.

The tracking module 30 may employ several different techniques forcapturing non-visual interaction data. On iOS, for example, the trackingmodule 30 can use the provided CoreMotion framework to receive processeddevice motion events and log the roll, pitch, yaw (the Euler angles) ofthe device. For efficiency, the tracking module 30 can throttle eventdelivery to two events per second or any arbitrary frequency. For layoutorientation on iOS, certain system notifications are notified when thehost application's 20 interface has changed orientation. One example ofsuch system notification isUIApplicationDidChangeStatusBarOrientationNotification. The trackingmodule 30 may record the presence of such system notifications. Otherdevice events, such as orientation change, may be captured utilizingstandard Android or iOS sensor APIs.

The tracking module 30 may capture visual and/or non-visual interactiondata at predetermined time intervals. With visual interaction data, thetracking module 30 may capture images from the mobile device 12according to any suitable pixel format. In one version, an objectreference to the top view in the view hierarchy (or the root view) isobtained and is written to a bitmap. The bitmap is writtenasynchronously to storage on the mobile device 12 for later processing.

In one technique for capturing visual interaction data with the trackingmodule 30, a reference to all of the host application's 20 UI Windowobjects is acquired. The UI Window objects include backing layers thatare rendered in order from back to front to a graphics context, such asCGContextRef. The backing layers then create a UI Image object from thegraphics context. The rendering operation traverses the layer tree andrenders all sublayers. In one embodiment, the coordinate system's originis oriented to the bottom-left. Alternatively, the coordinate system'sorigin may be oriented to the top-left. A simple transform and/orscaling may be required before drawing the layer to the graphicscontext.

As shown in FIGS. 2, 4, and 5, the mobile device 12 generally includes amain user interface (UI) thread 36, which provides main execution pathsfor handling user input to the mobile device 12 and responding to userevents on the mobile device 12. The main UI thread 36 is the main threadof execution for the host application 20. System calls to the hostapplication 20 are performed in the main UI thread 36 and mostapplication code for the host application 20 is run in the main UIthread 36.

As shown in FIG. 4, the main UI thread 36 is accessed by the operatingsystem 34 of the mobile device 12 during certain periods of time. Forsimplicity, the range of time during which the operating system 34 mayaccess the main UI thread 36 is identified between TO and TI 0 in FIG.4.

In one approach, the tracking module 30 captures the visual interactiondata by accessing the main UI thread 36 during these certain periods oftime. However, doing so may adversely affect the availability of themain UI thread 36 to the operating system 34 during certain times. Morespecifically, in one embodiment, the operating system 34 and thetracking module 30 simultaneously maintain access to the main UI thread36. However, such simultaneous access may cause burden to the main UIthread 36. Alternatively, the main UI thread 36 may allow access toeither the operating system 34 or the tracking module 30, but not both.Nevertheless, in such instances, the operating system 34 may be deprivedfull access to the main UI thread 36. As such, poor performance of theoperating system 34, and consequently, user interaction with the hostapplication 20, may result during capture of the visual interaction datawhen the tracking module 20 is accessing the main UI thread 36.

In order to keep user interactions with the host application 20 fluidand uninterrupted, the tracking module 30 may additionally oralternatively capture interaction data by utilizing a second thread 37that is independent of the main UI thread 36, as shown in FIG. 5. Thesecond thread 37 may be provided by any suitable medium, including thehost application 20, the tracking module 30, or the mobile device 12. Byutilizing the second thread 37, much of the processing involved withcapturing interaction data occurs off the main UI thread 36. As such,the adverse effects on the performance of the mobile device 12,operating system 34, and/or host application 20 are minimized.

In some instances, rendering UIView objects to a graphics context mustbe a “thread-safe” operation. Specifically, for multi-threaded programs,code is thread-safe if it manipulates shared data structures in a mannerthat guarantees safe execution by multiple threads at the same time. Forcertain operating systems 34, some views, e.g., UIWebView, do notguarantee thread safety. Therefore, steps must be taken to ensure threadsafety. Otherwise, host application 20 instability can result.

In one embodiment, the tracking module 30 employs a swizzling techniquefor manipulating computer methods is utilized to ensure thread safetyand minimize adverse affects on performance of the main UI thread 36during capture of interaction data. The swizzling technique can overrideor intercept such source code by utilizing a dynamic runtime system,such as Objective-C runtime. The visual interaction data is captured asan object that represents the image on the screen. When a message issent to the object, the dynamic runtime system finds a firstimplementation for a selector for a specific class and invokes the firstimplementation with a first functionality. The dynamic runtime systemenables the tracking module 30 to override the first implementation witha supplemental implementation providing replacement functionality.However, the first implementation may still be invoked. As such,original methods in the source code may be “patched” with a supplementalmethod while still making use of the original method.

In one embodiment, the swizzling technique “intercepts” rendering callsto a method, such as renderInContext. If the operating system 34attempts to render an unsafe layer, e.g. TileHostLayer, the operatingsystem 34 copies all properties necessary for rendering, such asposition, bounds, contents, and sublayers to a new layer and rendersthat layer instead. Intercepting rendering calls is not trivial. Asstated above, the rendering operation traverses a layers' sublayers andrenders those layers and their sublayers (and their sublayers, etc) in arecursive manner to the bitmap in the order in which they appear in theobject hierarchy. The rendering method may be provided by an operatingsystem software development kit (SDK) and often cannot be altered forpurposes of implementing the swizzling technique. However, Objective-Cis one of the programming languages supported by the operating system 34runtime, e.g., iOS runtime. As such, Objective-C runtime is dynamicallowing the suspension of altering the rendering until runtime. InObjective-C, a method's selector and implementation are not bound atcompile time but rather at runtime. When a message is sent to an object,the runtime system finds the implementation for a selector for aspecific class (since selectors are not necessarily unique) and invokesthat implementation. So, using the method_exchangeImplementationsprovided by the Objective-C runtime, the original renderInContext:implementation can be switched with a replacement that provides newfunctionality, while still allowing the original implementation to beinvoked.

In one example, FIG. 5 illustrates by way of arrows between the secondthread 37 and the main UI thread 36 how the swizzling technique iscarried out. As shown, points at which arrows leave the second thread 37are intended to represent moments at which the tracking module 30initiates and reinitiates the swizzling technique. Points at which thearrows reach the main UI thread 36 are intended to represent moments atwhich the swizzling technique overrides the operating system 34 sourcecode. Unlike FIG. 4, the operating system 34 substantially maintainsfull access to the main UI thread 36. FIG. 5 is intended a simplifiedrepresentation of swizzling technique and is not intended to limit theswizzling technique. In other words, the swizzling technique may beillustrated according to various other representations without departingfrom its intended purpose.

Captured interaction data may be stored to the mobile device 12. Asillustrated in FIG. 2, the mobile device 12 includes a mobile devicestorage 40, which is in communication with the host application 20and/or tracking module 30. Captured interaction data may be stored intothe mobile device storage 40. Both visual and non-visual capturedinteraction data may be stored in the mobile device storage 40. Forinstance, Bitmap files of images captured during operation of the hostapplication 20 may be stored in the mobile device storage 40. Storage ofcaptured interaction data in the mobile device storage 40 may bepermanent or temporary. The mobile device storage 40 may be anyconventional data storage device such as a hard disk or solid-statememory. The mobile device storage 40 is embedded generally within themobile device 12. In other embodiments, captured interaction data may bestored in a buffer associated with the host application 20 and/ortracking module 30. Such a buffer may be implemented as any suitablebuffer configuration known in the art.

The interaction data may be processed. Processing of the interactiondata may occur after data is captured. Alternatively, processing ofinteraction data may occur while interaction data is captured.Processing of the captured interaction data is conducted generally inpreparation for transmitting the captured interaction data to thetracking server 16, as will be discussed below. Specifically, capturedinteraction data, and more specifically captured visual interactiondata, may consume large amounts of computer resources, such as memory.Therefore, transmission of captured interaction data from the mobiledevice 12 may be inefficient without processing. The capturedinteraction data is generally processed on the mobile device 12 itself.

Processing the captured interaction data may be further defined asencoding the captured visual interaction data. As shown at step 74 inFIG. 3, the captured visual interaction data is encoded. The capturedvisual interaction data is encoded for enabling efficient transmissionof the captured interaction data from the mobile device 12 to thetracking server 16. As shown in FIG. 2, the mobile device 12 includes anencoder 44 in communication with the host application 20, trackingmodule 30, and/or mobile device storage 40.

The tracking module 30 captures visual interaction data and provides thecaptured visual interaction data to the encoder 44. The encoder 44 maybe separated from the tracking module 30, as shown in FIG. 2.Alternatively, the encoder 44 is integrated with the tracking module 30.In such instances, the tracking module 30 encodes the captured visualinteraction data. The tracking module 30 may be further defined as amovie-encoding API.

There are different techniques to encode and store the session visualsin the buffer for later transmission to the tracking server 16. Theselection of these techniques can depend on many factors. Generally,complete bitmaps are not stored for every moment of interaction in asession because doing so would consume too many bytes of memory andconsequently making transmission of these large payloads to the serverdifficult. Instead, visual changes are reduced, summarized, andcompressed using a variety of techniques so that it can be reconstitutedlater for eventual replay of the session.

In one embodiment, the tracking module 30 is configured to measure howlong it takes to capture visual interaction data. Based on themeasurement, the tracking module 30 may dynamically increase or decreasefrequency at which visual interaction data is captured. In doing so, thetracking module 30 maximizes performance of the host application 20, andultimately the user's 22 experience with the host application 20.

In another embodiment, the step of encoding the captured visualinteraction data is further defined as compressing the captured visualinteraction data into a video. In this embodiment, the encoder 44compresses images into the video using a codec. The video may be anysuitable format, such as MPEG, MP4, AVI, WMV formats. Alternatively, thevideo may be an animated reproduction. The encoder 44 may utilize anysuitable compression algorithm or program, such as a codec, forcompressing the captured visual interaction data. The captured visualinteraction data may be continuously compressed into the video in“real-time.” In such instances, compression generally occurssimultaneously as the user 22 interacts with the host application 20 orimmediately after the visual interaction data is captured on the mobiledevice 12. In other words, compression occurs piecemeal orintermittently. Alternatively, visual interaction data may be compressedat one time once requisite visual interaction data is captured. In suchinstances, all of the captured visual interaction data is provided tothe encoder 44 at once.

In addition to visual interaction data, non-visual interaction data maybe included in the video. For example, timing events associated withinteraction data may be stored and inserted into corresponding frames ofthe video. The video may be stored in the mobile device storage 40 orbuffer. As will be described below, the video may be retrieved andtransmitted from the mobile device 12 to the tracking server 16.

On certain operating systems 34, such as iOS, once the tracking module30 captures the screen to a UIImage, the tracking module 30 may useframeworks, such as AVFoundation and CoreVideo, which are provided bythe operating system 34 to create a movie in real-time. When thetracking module 30 captures a screenshot, the time interval since thebeginning of the session is noted and the frame is inserted into themovie at the correct time. When dealing with images on iOS, care must betaken to ensure the pixel information is consistent and predictable. Forexample, when creating the movie, the tracking module 30 can optionallyspecify 32-bit pixels format, such as BGRA8888. By doing so, thetracking module 30 ensures that it defines that bitmap with the samepixel format when the tracking module 30 captures the screen to a bitmapcontext.

In yet another embodiment, the step of encoding the captured visualinteraction data is further defined as applying an image differencingprocess. The image differencing process compares captured visualinteraction data. The image differencing process may be implemented byany suitable component(s) and/or process implemented with the mobiledevice 12. In one embodiment, the image differencing process isimplemented by the tracking module 30.

In some cases, the image differencing process is better suited for aparticular device for compressing the visual information into thebuffer. This image differencing process can be summarized as imagedifferencing or “diffing.” In one embodiment, the result of the imagedifferencing process is a collection of smaller images representing onlythe portions of the screen captures that have changed (the “diffs”) andall the accompanying metadata that would be required to re-create thefull image at any given point in time.

As shown in FIG. 6, the image differencing process operates by assigninga coordinate space to a first image 46 and a second image 48 such that acoordinate origin of both images is 0,0. The image differencing processthen compares the first and second images 46, 48 for differences. Morespecifically, the first and second images 46, 48 are formed of aplurality of pixels 49 and the image differencing process compares thefirst and second images 46, 48, one pixel 49 at a time. In someembodiments, for performance considerations, a heuristic can be appliedto compare a subset of pixels 49 by omitting some pixels 49 during thecomparison phase.

The first and second images 46, 48 generally have the samecharacteristics, such as dimension or area, for enabling efficientcomparison. For example, in FIG. 6, the first image 46 defines a heighth1 and a width w1 and the second image defines a height h2 and a widthw2, whereby h1=h2, and w1=w2.

However, in some cases, as shown in FIG. 7, the first and second images46, 48 may have different dimensions. The first image 46 may have anarea 46 a that does not overlap with the second image 48. Similarly, thesecond image 48 may have an area 48 a that does not overlap with thefirst image 46. At this stage, the image differencing technique mayrecognize the areas 46 a and 48 a as differences between the first andsecond images 46, 48. The image differencing technique may do so foroptimizing the process by eliminating a need for further analysis ofareas 46 a and 46 b. FIG. 7 is provided merely for illustrative purposesand the term “overlap” is not intended to require that the first andsecond images 46, 48 be physically overlapping.

The common area of overlap between the first and second images 46, 48 isdefined as a focus area 50, as shown in FIGS. 7 and 8. The focus area 50may have various dimensions depending upon the size of the common areaof overlap. The image differencing technique divides the focus area 50into regions 52 that are smaller than the focus area 50. The regions 52dividing the focus area 50 generally have a common size. The regions 52may have any suitable size and the size of the regions 52 may dependupon various factors including, but not limited to, screen size andscreen resolution. In one example, the dimensions of the focus area 50are 300×300 pixels and the focus area 50 is divided into regions 52 of50×50 pixels.

Each region 52 may include any suitable number of pixels 49. Forexample, each region 52 may include between 10 and 100 pixels. Thepixels 49 may be further defined as square pixels 49. In FIG. 8, thepixels 49 may not be drawn to scale and are represented for only one ofthe regions 52 for simplicity in illustration. If the size of theregions 52 is too small, performance of the component(s) and/orprocesses implementing the image differencing process may be adverselyimpacted. Yet, if the size of the regions 52 is small, the imagedifferencing process may more accurately detect differences between thefirst and second images 46, 48.

Each region dividing the focus area 50 is examined pixel-by-pixel 49.Each pixel 49 includes a red, green, and blue color component. Each ofthe color components of each pixel 49 has an intensity level. In oneembodiment, the image differencing process compares the intensity levelsfor each of the color components of each pixel 49 in a specified region52 of the focus area 50. Specifically, the image differencing processdetects differences in the intensity levels of color components betweena pixel 49 in the first image 46 and color components of a correspondingand overlapping pixel 49 in the second image 48. In one example, theimage differencing process detects differences by subtracting theintensity levels of each of the color components of the pixel 49 infirst image 46 from the corresponding intensity levels of each of thecolor components of the pixel 49 in the second image 48 to determine adifference in intensity level for each color component. Thereafter, thedifference in intensity levels for each of the color components isdivided by a total possible number of colors available for the specificcolor component. In one example, the total possible number of colorsavailable for each color component is 256. The image differencingprocess then establishes a difference value, such as a percentage value,representing the difference in intensity level.

The image differencing process continues to examine each of the pixels49 in the specified region 52 according to this method. In oneembodiment, the image differencing process establishes a totaldifference in the intensity level for the specified region 52 as a wholebased on all of the pixels 49 in the specified region 52. The imagedifferencing process may establish the total difference in intensitylevel by combining the calculated difference values for the pixels inthe specified region 52. However, the total difference in intensitylevel may be established according to other methods.

The image differencing process determines whether the total differencein the intensity level is greater than a predefined threshold percentagedifference. In one embodiment, the predefined threshold percentagedifference is defined as at least a 2% difference. If the totaldifference in the intensity level is greater than the predefinedthreshold percentage difference, the specified region 52 is recognizedas having a notable image difference between the first and second images46, 48. In such instances, imaging data, such as bitmap data, for thespecified region 52 is saved to the mobile device storage 40. In oneembodiment, the imaging differencing process stops analyzing the pixels49 in the specified region 52 once the predefined threshold percentagedifference is reached. The imaging differencing process may do so toreduce the number of pixels 49 being analyzed thereby maximizingperformance of the imaging differencing process. Thereafter, the imagingdifferencing process continues to a subsequent region 52 in the focusarea 50 to examine the subsequent region 52. The subsequent region 52may be adjacent to the previously analyzed region 52. The imagingdifferencing process continues analyzing the regions 52 in such afashion until each of the regions 52 in the focus area 50 is analyzed.

Each region 52 recognized as not having a notable image difference isdiscarded or unused. Each region 52 recognized as having a notable imagedifference is cropped out of the focus area 50 and saved as a differenceimage in the mobile device storage 40. On the Android operating system,for example, this may be performed in Java. For better performance,however, a native implementation using a JNI bridge could be employed.On iOS, this may be implemented in Objective-C but could also beimplemented in C for better performance. These optimizations aresometimes referred to as writing the algorithm in “native” code.

The difference images are significantly smaller than the first andsecond images 46, 48 and allow efficient transmission of the visualinteraction data to the tracking server 16, as will be described below.

In some instances, a first difference image and a second differenceimage may include common imaging data such that the first and seconddifference images overlap. In such instances, the first and seconddifference images may be combined. In other instances, difference imagesthat were derived from adjacent regions 52 in the focus area 50 may becombined. In either instance, combining difference images may be done inorder to minimize info nation that is stored in the mobile devicestorage 40.

The imaging differencing process may compare and analyze the pixels 49of the specified region 52 according to other methods not specificallyrecited herein without departing from the scope of the invention.Furthermore, the imaging differencing process may be implementedaccording to any suitable programming language or framework, such asJava, Java Native Interface (JNI), C, or Objective C.

In one embodiment, the tracking module 30 itself implements the imagedifferencing process. In another embodiment, the tracking module 30 mayenlist a service process for implementing the image differencingprocess. In one embodiment, the service process is called from withinthe host application 20. In most cases, the tracking module 30 and/orservice process implement the image differencing process on a separatethread from the main UI thread 36. In other words, the captured imagesare compared for differences on the separate thread as opposed to themain UI thread 36. By doing so, overuse of the main UI thread 36 isprevented and responsiveness of the mobile device 12, operating system34, and host application 20 are maximized.

One example of the service process for implementing the differencingprocess is IntentService by Android®. The service process generallyreceives a request to add a task. The task is added to a queue and theservice process operates in the background to complete the task. Forexample, a request may be sent to the service process once the first andsecond images 46, 48 are captured and/or stored. The request may includeidentifying information associated with the first and second images 46,48. The service process may then implement the imaging differencingprocess. The service process will then apply the image differencingprocess to produce a set of smaller images as well as the necessarymetadata. The metadata may contain information relating to where (i.e.coordinate information) and when (i.e. timestamp) the set of diffsshould be applied. The service process may implement the imagedifferencing process according to other techniques not specificallyrecited herein.

The captured non-visual interaction data may be synchronized withcorresponding difference images resulting from the image differencingprocess. In one embodiment, the service process associates meta-datawith each difference image resulting from the imaging differencingprocess. Such meta-data may include information such as coordinateinformation and timestamp information associated with each differenceimage.

Another step in the process of recording the visual interactioninformation may be masking, or redacting, portions of the image beforeit is transmitted to the tracking server 16. One reason for doing so isto prevent personally identifiable, or private, information (e.g. usernames, passwords, credit card numbers) from being included in the visualinteraction data that is transmitted to the tracking server 16. Screencaptures containing such personal information need to be “masked” sothat such information is no longer visible when the session iseventually watched. In addition, masking ensures that such informationis not transmitted over networks and stored permanently anywhere.

There is a variety of ways of achieving masking. On the iOS platform, tomask an arbitrary view or control in an application, the tracking module30 can use the swizzling technique described earlier. In the“intercepted render” method, if the tracking module 30 is rendering thebacking layer for a view that has been registered as needing masking,the tracking module 30 instead draws a black box with the same positionand bounds as the original view. The visual elements chosen for maskingcan be configured at compile-time by passing references to thoseelements to the tracking module 30, or by defining XPATH's to thoseelements.

Alternatively, masking can be performed by “walking” the view hierarchy(starting from the root view) and inspecting each of the nodes in theview tree. If a subview (for example, a text box, or label) isencountered and is marked for masking, a mask is drawn on the screencapture at the coordinates of the subview.

If the text field being edited has been identified as needing to beredacted or masked, it is important to also obscure any onscreenkeyboard graphics that appear. On some devices, particularly on iOS, anon-screen keyboard appears. What is being typed can be inferred byobserving the keys that are pressed on the onscreen keyboard.Consequently, onscreen keyboard graphics must be obscured from thecollected image data in the buffer. When drawing the keyboard to thecaptured bitmaps, the tracking module 30 can check if the currentlyediting view is masked/secure (in the same swizzled renderInContext:method) and if so, draw a placeholder with the same dimensions as thekeyboard on top of the position on the screen where the keyboard wouldappear. The tracking module 30 may mask personally identifiable regionsaccording to other methods not specifically described herein.

The process of capturing and processing visual interaction data can befairly resource intensive and may not be supported on all mobile devices12. In order to avoid adversely affecting the user experience, thetracking module 30 may capture data regarding the ability of the mobiledevice 12 to perform the necessary processing. Such data can be analyzedto determine whether the parameters for session capture, for example,the capture rate, should be adjusted. For mobile devices 12 failing tomeet a certain threshold of capability, session capture can be disabled.Some of the data that can be captured and examined includes, but is notlimited to, maximum available heap size, “memory class”, execution timefor diffing and cropping tasks.

Other techniques can be used to minimize the CPU overhead during captureand processing. For example, images can be downsampled (shrunk down) sothat both payload size and processing overhead are minimized. Thefrequency of image capture and the rate at which images are processedcan be altered to avoid interfering with user experience and to maximizebattery life of the device. Alteration of the frequency of image capturecan apply for the image differencing technique and/or movie encoding.

Additionally, dynamic performance monitoring can be instrumental inadjusting the use of these techniques for optimal performance. Bymeasuring the time consumed by image capture and/or processing thefrequencies of work and the degree to which optimization techniques areutilized can be automatically increased or decreased.

At step 76, the captured interaction data, including visual and/ornon-visual interaction data, is transmitted from the mobile device 12 tothe tracking server 16. In one embodiment, the tracking module 30initiates transmission of the captured interaction data. The mobiledevice 12 includes standard computing components to facilitatetransmission of the captured interaction data to the tracking server 16.As mentioned above, once the captured interaction data is processedaccording to any of the aforementioned methods, the processedinteraction data may be stored in the mobile device storage 40 or thebuffer.

Before transmission, the captured interaction data may be retrieved fromthe mobile device storage 40 or buffer. In one embodiment, the trackingmodule 30 retrieves the stored interaction data. Transmission ofcaptured interaction data may be further defined as transmission of thebuffer including the captured interaction data. In one embodiment,transmission to the tracking server 16 occurs periodically orintermittently. However, transmission may occur at one time.

Transmission of captured interaction data may occur with knowledge ofthe user 22 through the presentation of a process dialog. In suchinstances, the captured interaction data in the buffer can betransmitted in a foreground thread. Alternatively, transmission ofcaptured interaction data may occur in background processes withoutknowledge of the user 22.

The captured interaction data may have any suitable format before orduring transmission. Because transmission possibly may be long runningand asynchronous, on iOS it can be advantageous to initiate along-running background task to prevent the OS from suspending thoseoperations using the UIApplicationbeginBackgroundTaskWithExpirationHandler: method.

In one embodiment, captured interaction data is transmitted to thetracking server 16 only upon the occurrence of a predetermined event.The predetermined event may be defined as the user 22 having experienceda particular feature. In one example, the user 22 may be invited fromthe host application 20 to complete a survey. Once the user 22 acceptsthe invitation and completes the survey, the predetermined event isdeemed to have occurred and the captured interaction data may betransmitted. The predetermined event may also be defined as the user 22using the host application 20 for a predefined threshold of time, suchas a minimum threshold of time. The predetermined event may be definedas the detection of a specified type of internet connection, such asWi-Fi. In such embodiments, detection of Wi-Fi before transmission maybe important to reduce cellular carrier data usage by the mobile device12. If the specified type of internet connection is not available, thecaptured interaction data may be stored in the mobile device storage 40until such connection becomes available. The predetermined event mayalso be the detection of any mobile device 12 command or status. In oneexample, transmission occurs only when the mobile device 12 is idle.

For performance and efficiency, the captured visual interaction data inthe buffer can be transmitted as a collection of images and accompanyingmetadata in a compressed format (e.g. zip file format). The capturednon-visual interaction data in the buffer can be transmitted as text ina JSON format. There are alternate formats and configurations totransmit the buffer, however.

At step 78, the captured interaction data is received on the trackingserver 16. The tracking server 16 includes standard computing componentsknown in the art for receiving captured interaction data from the mobiledevice 12 and sending captured interaction data to the analysis computer18. The step of receiving captured interaction data may be furtherdefined as receiving the buffer including the captured interaction data.

As shown in FIG. 1, the tracking server 16 is connected to a trackingstorage 54. The tracking storage 54 may include a conventional datastorage device, such as a hard disk or solid-state memory. The trackingstorage 54 may be located within and connected directly to trackingserver 16. Alternatively, the tracking storage 54 may be a remote datastorage facility connected to tracking server 16, such as a database. Ineither instance, the received interaction data is generally provided tothe tracking storage 54 in preparation for further processing, as willbe described below.

When the captured interaction data in the buffer has been completelyreceived by the tracking server 16, or when enough interaction data hasbeen received such that there is enough interaction data to reconstitutea contiguous portion of a session, some processing needs to occur toconvert it into a format that can be replayed.

At step 80, the captured interaction data received on the trackingserver 16 is converted into a visual representation. In one embodiment,the visual representation is a representation of the user's 22interaction with the host application 20. The visual representation maybe defined as a video file. The video file may have any video formatknown in the art, such as AVI, WMV, or MP4. The visual representationmay additionally include audio relating to the host application 20.

The system 10 may include a processing module 56 for converting thereceived interaction data into the visual representation. The processingmodule 56 is in communication with the tracking server 16. In oneembodiment, as shown in FIG. 1, the processing module 56 is separatefrom the tracking server 16. In another embodiment, the processingmodule 56 is integrated with the tracking server 16. The processingmodule 56 may retrieve the interaction data from the tracking storage54.

In other embodiments, processing of the interaction data for replay mayoccur on the mobile device 12 before transmission to the tracking server16. Thus, it is not necessary that processing of the interaction datafor replay occur on a tracking server 16. However, doing so on thetracking server 16 is advantageous to offload this work to a remotemachine to maximize performance on the mobile device 12.

The interaction data may be converted once all the interaction datarelating to the host application 20 is received at the tracking server16. Alternatively, interaction data may be converted once a predefinedrequisite amount of the interaction data is received at the trackingserver 16. The step 80 of converting the interaction data may be furtherdefined as converting the buffer including the interaction data.

The following approaches may be taken in instances where it is desiredto encode a self-contained video file, such as MPEG, WMV, AVI, FLV, etc.In some instances, the interaction data may be provided in a video filebefore the interaction data is received at the tracking server 16. Ifthe container format for the raw images in the buffer is already a videofile such as MPEG, WMV, AVI, FLV, etc., then the processing module 56can take one of many approaches. One approach is to extract images atregular time intervals from the video file using a third-party API, suchas FFMPEG or Microsoft Expression Encoder, and overlay touch symbols atthe appropriate places in the video. In such instances, the processingmodule 56 may extract images at regular time intervals from the videofile using an additional API or encoder. The processing module 56 maythen overlay touch symbols (such as round symbols or fingerprintsymbols) at appropriate places in the video and re-code the video usingthe additional API to create the visual representation.

In instances where captured visual interaction data includes differenceimages resulting from the image differencing process, the visualrepresentation is created according to another method. The differenceimages are reapplied to a canvas. In the processing stage, the rawimages are applied to the canvas that is the size of the original screenin the positions and sizes they were taken from originally. Time indexesassociated with the difference images may be analyzed and referenced.Any additional information such as touch indicators are overlaid on thecanvas and a processed image is finalized. The processed images areencoded in frames of the video using any suitable encoding API to createthe visual representation. The visual representation may be stored inthe tracking storage 54. The interaction data may be converted into thevisual representation using other methods not specifically recitedherein.

The following approaches may be taken in instances where it is desiredto encode an animated format, such as Flash or another custom animatedformat. Generally, features of the third-party API's are used to overlayanimations onto the existing movie, by video encoding. Animations may beoverlaid on to the video file by video encoding to create the visualrepresentation. An animation format such as Flash or other custom formatmay be utilized wherein the replay is achieved by overlaying raw imagesand animating elements, such as touches, on top of the raw images.

In the case where the source container format for the image data is avideo file such as MPEG, WMV, AVI, FLV, etc, then the processing module56 may extract images from the video using third party API's, such asFFMPEG or Microsoft Expression Encoder. Frames of video as bitmaps areextracted from the video at regular intervals, and the differencingalgorithm is performed on those images. The diff's are extracted fromthe bitmaps and those bitmaps are added to an event timeline. The eventtimeline can then be replayed in a custom player, such as a Flashplayer, Silverlight player, HTML5 based player, or similar.

In the case where the source container format is diff's, then the diffscan be directly transcoded into the final replayable format foranimation using their positions and time indices.

In either case, information such as touch events, orientation, anddevice information must be included in the final animation format attheir time indices so that this information can be represented in thefinal playback.

Once the visual representation is created, the analysis computer 18requests the visual representation from the tracking server 16, as shownat step 82. Generally, an end-user (not shown) interacts with theanalysis computer 18 to request the visual representation. However, thevisual representation may be requested independent of the end-user. Thevisual representation may be also retrieved from the tracking storage54. The visual representation may be requested and stored on theanalysis computer 18 according to any suitable method. In one example,the visual representation is downloaded from the tracking server 16 andsaved to memory in the analysis computer 18 for later viewing.

At step 84, the requested visual representation may be presented on theanalysis computer 18. The analysis computer 18 typically includes adisplay 58 for enabling the visual representation to be presented on theanalysis computer 18. The visual representation is generally presentedto the end-user through the display 58 of the analysis computer 18. Theend-user may examine the presented visual representation to gatherinformation regarding the captured interaction data. The visualinteraction is generally presented independent of the tracking server16. Furthermore, the visual representation may be presented on theanalysis computer 18 according to any suitable method. In one example,the visual representation is presented through a web browser.

One of the many advantages of the system 10 is to record enough visualand contextual data from the user's 20 session inside the hostapplication 20 to recreate at a later time a replay of the user's 20session for the purposes of usability analysis. Such replays can becoupled with survey responses to provide a full understanding of how auser 20 is using the host application 20, what issues a user 20 may behaving with the host application 20, and/or where there areopportunities for improving user satisfaction and overall usability.

The many features and advantages of the invention are apparent from thedetailed specification, and thus, it is intended by the appended claimsto cover all such features and advantages of the invention which fallwithin the true spirit and scope of the invention. Further, sincenumerous modifications and variations will readily occur to thoseskilled in the art, it is not desired to limit the invention to theexact construction and operation illustrated and described, andaccordingly, all suitable modifications and equivalents may be resortedto, falling within the scope of the invention.

What is claimed is:
 1. A computer-implemented method comprising thesteps of: embedding a tracking module in a host application, the hostapplication having been downloaded and installed on a mobile device andoperable independent from a web browser, the mobile device including anoperating system that accesses a main user interface (UI) thread,wherein the tracking module comprises an application programminginterface (API); capturing with the tracking module visual interactiondata relating to the host application wherein the visual interactiondata includes images presented on a display of the mobile device andwherein the tracking module captures the visual interaction data by:accessing the main user UI thread shared by the operating system;acquiring layers of an object from a user interface of the hostapplication; rendering layers of the object; and intermittentlyaccessing a second thread during capture of the visual interaction data,the second thread independent of the main UI thread, thereby increasingavailability of the main UI thread for the operating system duringcapture of the visual interaction data; and transmitting the visualinteraction data from the mobile device to a tracking server.
 2. Thecomputer-implemented method of claim 1 further including the trackingmodule: measuring a period of time required to capture the visualinteraction data; and dynamically altering a frequency at which thevisual interaction data is captured based on the measured period of timerequired to the capture interaction data.
 3. The computer-implementedmethod of claim 1 further including the tracking module: capturing datarepresenting the availability of resources of the mobile device; anddynamically altering a frequency at which the visual interaction data iscaptured based on the visual data representing the availability ofresources of the mobile device.
 4. The computer-implemented method ofclaim 1 further including: storing the visual interaction data to abuffer; and retrieving, with the tracking module, the visual interactiondata from the buffer.
 5. The computer-implemented method of claim 1further including the tracking module compressing the visual interactiondata in preparation for transmission.
 6. The computer-implemented methodof claim 5 wherein compressing further includes the tracking moduleapplying an image differencing process to capture visual differencesbetween sequential interaction data.
 7. The computer-implemented methodof claim 1 wherein transmitting the visual interaction data includestransmitting the visual interaction data upon occurrence of apredetermined event including at least one of detection of apredetermined network connection for the mobile device, a detection of acommand or status of the mobile device, an acceptance of an invitationto complete a survey, and a completion of a survey.
 8. Thecomputer-implemented method of claim 1 further including masking, withthe tracking module, portions of the visual interaction data beforetransmission.
 9. The computer-implemented method of claim 1, furthercomprising capturing, with the tracking module, non-visual interactiondata relating to the host application, the non-visual interaction datacomprises meta-data including at least one of touch-event data, gesturedata, GPS coordinate data, audio data, accelerometer data, orientationdata, camera data, barometer data, and compass data.
 10. Thecomputer-implemented method of claim 1 wherein capturing the imagesincludes capturing a change to the images wherein the change isinitiated by a user.
 11. The computer-implemented method of claim 1wherein capturing the images includes capturing a change to the imageswherein the change is initiated by the host application independent of auser.
 12. A non-transitory computer-readable medium comprisinginstructions, which when executed by one or more processors, implement atracking module, the tracking module configured to be embedded in a hostapplication, the host application having been downloaded and installedon a mobile device and operable independent from a web browser, themobile device including an operating system that accesses a main userinterface (UI) thread, the tracking module comprising an applicationprogramming interface (API) and being configured to: capture visualinteraction data relating to the host application wherein the visualinteraction data includes images presented on a display of the mobiledevice, and to capture the visual interaction data, the tracking moduleis configured to: access the main user UI thread shared by the operatingsystem; acquire layers of an object from a user interface of the hostapplication; render layers of the object; and intermittently access asecond thread during capture of the visual interaction data, the secondthread that is independent of the main UI thread; and transmit thevisual interaction data from the mobile device to a tracking server. 13.The non-transitory computer-readable medium of claim 12, wherein thetracking module is further configured to: measure a period of timerequired to capture the visual interaction data; and dynamically alter afrequency at which the visual interaction data is captured based on themeasured period of time required to the capture interaction data. 14.The non-transitory computer-readable medium of claim 12, wherein thetracking module is further configured to: capture data representing theavailability of resources of the mobile device; and dynamically alter afrequency at which the visual interaction data is captured based on thevisual data representing the availability of resources of the mobiledevice.
 15. The non-transitory computer-readable medium of claim 12,wherein the tracking module is further configured to: store the visualinteraction data to a buffer; and retrieve, with the tracking module,the visual interaction data from the buffer.
 16. The non-transitorycomputer-readable medium of claim 12, wherein the tracking module isconfigured to compress the visual interaction data in preparation fortransmission.
 17. The non-transitory computer-readable medium of claim16, wherein the tracking module is configured to compress the visualinteraction data by applying an image differencing process to capturevisual differences between sequential interaction data.
 18. Thenon-transitory computer-readable medium of claim 12, wherein thetracking module is further configured to transmit the visual interactiondata upon occurrence of a predetermined event including at least one ofdetection of a predetermined network connection for the mobile device, adetection of a command or status of the mobile device, an acceptance ofan invitation to complete a survey, and a completion of a survey. 19.The non-transitory computer-readable medium of claim 12, wherein thetracking module is further configured to mask portions of the visualinteraction data before transmission.
 20. The non-transitorycomputer-readable medium of claim 12, wherein the tracking module isfurther configured to capture non-visual interaction data relating tothe host application, the non-visual interaction data comprisesmeta-data including at least one of touch-event data, gesture data, GPScoordinate data, audio data, accelerometer data, orientation data,camera data, barometer data, and compass data.